Selected Variables

base: Code of the patient
covariates:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Previous surgery - LEV
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
outcomes_ql:
- 2Y. ODI - Score (%)
- 2Y. SRS22 - SRS Subtotal score
- 2Y. SF36 - MCS
- 2Y. SF36 - PCS
outcomes_radiology:
- 6W. Major curve Cobb angle
- 1Y. Major curve Cobb angle
- 6W. T1 Sagittal Tilt
- 1Y. T1 Sagittal Tilt
- 6W. Sagittal Balance
- 1Y. Sagittal Balance
- 6W. Global Tilt
- 1Y. Global Tilt
- 6W. Lordosis (top of L1-S1)
- 1Y. Lordosis (top of L1-S1)
- 6W. LGap
- 1Y. LGap
- 6W. Pelvic Tilt
- 1Y. Pelvic Tilt
- 6W. RSA
- 1Y. RSA
- 6W. RPV
- 1Y. RPV
- 6W. RLL
- 1Y. RLL
predictive:
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Osteotomy
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Tobacco use_First Visit
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
demographic:
- Age
- Gender
- Prior Spine Surgery
- ASA classification
- 3CO
- BMI_First Visit
- Global Tilt
- ideal LL
- Lordosis (top of L1-S1)
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
expanded:
- Age
- Gender
- Prior Spine Surgery
- '1st surgeon: experience in ASD surgery'
- ASA classification
- Decompression
- Osteotomy
- 3CO
- SPOs
- BMI_First Visit
- Tobacco use_First Visit
- Osteoporosis / osteopenia
- Previous surgery - LEV
- LGap
- RLL
- Cobb LS curve (Degree)
- Number of Interbody Fusions
- 'Posterior Instrumented Fusion: Upper / Lower Levels'
- Alif
- LL-Lordosis Difference
- Weight (kgs)_First Visit
- Height (cm)_First Visit
- Total surgical time st1+st2+st3
- Alcohol/drug abuse
- Anemia or other blood disorders
- Osteoarthritis
- Mild vascular
- Depression / anxiety
- Diabetes with end organ damage
- Cardiac
- Hypertension
- Chronic pulmonary disease
- Nervous system disorders
- Renal
- Peripheral vascular disease
- Psychiatric / Behavioral
- Peptic ulcer
- Bladder incontinence
- Bowel incontinence
- Leg weakness
- Loss of balance
- NRS back - Leg pain - Average
- Years with spine problems
- ODI - Score (%)_First Visit
- SRS22 - SRS Total score_First Visit
- SF36 - PCS_First Visit
- SF36 - MCS_First Visit
- Major curve Cobb angle
- SRS22 - SRS Subtotal score_First Visit
- T1 Sagittal Tilt
- Sagittal Balance
- Global Tilt
- Lordosis (top of L1-S1)
- Pelvic Tilt
- RSA
- RPV

Propensity Scores Common Support

Model Stats

  • Treatment proportion: 0.123
  • Model Type: elastic_net
  • Accuracy: 0.8946931
  • Params: alpha: 0.3307692 lambda: 0.010492

Average Treatment Effects - Radiology

Outcome: 6W. Major curve Cobb angle
Distribution:
      0%      25%      50%      75%     100% 
-72.0000 -20.0000 -10.2200  -3.0325  27.5500 
Model Type Y: boosting 
RMSE: 16.7497293841677 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 12.9493946872086 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -0.214 (Std.Error: 4.534)
Trimmed ATE (Yes-No): 0.183 (Std.Error: 4.702)
Upper ATE (Yes-No): -11.407 (Std.Error: 3.682)
Observational differences in treatment 2.425 (Yes-No) 

   treatment  outcome
1:       Yes 22.89275
2:        No 20.46777
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Major curve Cobb angle
Distribution:
    0%    25%    50%    75%   100% 
-64.00 -22.00  -9.00  -1.45  22.44 
Model Type Y: boosting 
RMSE: 18.7947717542362 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 14.4723639222115 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): 3.627 (Std.Error: 3.451)
Trimmed ATE (Yes-No): 4.159 (Std.Error: 3.552)
Upper ATE (Yes-No): -10.07 (Std.Error: 4.908)
Observational differences in treatment 3.108 (Yes-No) 

   treatment  outcome
1:       Yes 23.52800
2:        No 20.42007
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-23.631420  -6.000000  -1.457698   1.634417  18.000000 
Model Type Y: boosting 
RMSE: 5.25369515881882 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

Model Type No: boosting 
RMSE: 6.09637117511365 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -6.089 (Std.Error: 1.11)
Trimmed ATE (Yes-No): -6.216 (Std.Error: 1.124)
Upper ATE (Yes-No): -2.368 (Std.Error: 4.465)
Observational differences in treatment -0.963 (Yes-No) 

   treatment   outcome
1:       Yes -3.692905
2:        No -2.730217
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. T1 Sagittal Tilt
Distribution:
        0%        25%        50%        75%       100% 
-30.098675  -5.293893  -2.000000   1.311482  20.000000 
Model Type Y: boosting 
RMSE: 6.36184902743723 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 5.87627142648511 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -3.366 (Std.Error: 1.797)
Trimmed ATE (Yes-No): -3.34 (Std.Error: 1.859)
Upper ATE (Yes-No): -3.922 (Std.Error: 2.928)
Observational differences in treatment 0.373 (Yes-No) 

   treatment   outcome
1:       Yes -2.211871
2:        No -2.585074
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Sagittal Balance
Distribution:
      0%      25%      50%      75%     100% 
-194.790  -68.985  -28.930    0.950  114.150 
Model Type Y: boosting 
RMSE: 52.5101740607718 
Params: nrounds: 100.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 52.7794685433082 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -48.206 (Std.Error: 12.698)
Trimmed ATE (Yes-No): -50.109 (Std.Error: 13.103)
Upper ATE (Yes-No): -3.191 (Std.Error: 28.381)
Observational differences in treatment -7.524 (Yes-No) 

   treatment  outcome
1:       Yes 25.28385
2:        No 32.80798
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Sagittal Balance
Distribution:
       0%       25%       50%       75%      100% 
-237.4700  -62.5225  -28.1650    6.4125  109.5400 
Model Type Y: boosting 
RMSE: 50.3091976370015 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 51.1389029817983 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.875

ATE (Yes-No): -39.274 (Std.Error: 10.766)
Trimmed ATE (Yes-No): -38.727 (Std.Error: 11.142)
Upper ATE (Yes-No): -50.941 (Std.Error: 22.87)
Observational differences in treatment -0.815 (Yes-No) 

   treatment  outcome
1:       Yes 37.53697
2:        No 38.35226
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Global Tilt
Distribution:
      0%      25%      50%      75%     100% 
-68.6200 -18.0175  -6.0900   1.6025  16.0000 
Model Type Y: boosting 
RMSE: 14.6475670600561 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 12.3318452107063 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -9.385 (Std.Error: 3.961)
Trimmed ATE (Yes-No): -9.422 (Std.Error: 4.131)
Upper ATE (Yes-No): -8.404 (Std.Error: 6.127)
Observational differences in treatment -5.198 (Yes-No) 

   treatment  outcome
1:       Yes 19.00525
2:        No 24.20356
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Global Tilt
Distribution:
     0%     25%     50%     75%    100% 
-62.630 -15.825  -5.100   1.000  26.000 
Model Type Y: boosting 
RMSE: 15.3980734089774 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 11.4132822257368 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -14.692 (Std.Error: 3.624)
Trimmed ATE (Yes-No): -14.814 (Std.Error: 3.73)
Upper ATE (Yes-No): -11.834 (Std.Error: 7.127)
Observational differences in treatment -3.243 (Yes-No) 

   treatment  outcome
1:       Yes 22.58559
2:        No 25.82885
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Lordosis (top of L1-S1)
Distribution:
     0%     25%     50%     75%    100% 
-71.000 -24.000  -9.635   0.000  44.110 
Model Type Y: boosting 
RMSE: 17.4592131441212 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 15.4615325057658 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -5.565 (Std.Error: 4.915)
Trimmed ATE (Yes-No): -5.675 (Std.Error: 5.081)
Upper ATE (Yes-No): -2.454 (Std.Error: 5.899)
Observational differences in treatment -1.453 (Yes-No) 

   treatment   outcome
1:       Yes -50.61275
2:        No -49.15926
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Lordosis (top of L1-S1)
Distribution:
     0%     25%     50%     75%    100% 
-67.870 -24.405  -8.000   0.100  23.380 
Model Type Y: boosting 
RMSE: 23.0184897669382 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 14.8712618456256 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -10.625 (Std.Error: 4.324)
Trimmed ATE (Yes-No): -10.742 (Std.Error: 4.459)
Upper ATE (Yes-No): -7.643 (Std.Error: 9.049)
Observational differences in treatment 1.798 (Yes-No) 

   treatment   outcome
1:       Yes -47.25676
2:        No -49.05490
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. LGap
Distribution:
      0%      25%      50%      75%     100% 
-71.0000 -24.0000  -9.2566   0.2107  49.5964 
Model Type Y: boosting 
RMSE: 18.4922462596739 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 15.5952926002781 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -4.758 (Std.Error: 5.135)
Trimmed ATE (Yes-No): -4.886 (Std.Error: 5.322)
Upper ATE (Yes-No): -1.179 (Std.Error: 6.006)
Observational differences in treatment -2.381 (Yes-No) 

   treatment  outcome
1:       Yes 11.22173
2:        No 13.60306
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. LGap
Distribution:
       0%       25%       50%       75%      100% 
-67.72420 -24.48880  -7.14660   0.68115  22.08000 
Model Type Y: boosting 
RMSE: 20.4487816321054 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 14.9853997912591 
Params: nrounds: 100.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

ATE (Yes-No): -11.134 (Std.Error: 5.689)
Trimmed ATE (Yes-No): -11.225 (Std.Error: 5.92)
Upper ATE (Yes-No): -8.84 (Std.Error: 8.275)
Observational differences in treatment -0.197 (Yes-No) 

   treatment  outcome
1:       Yes 13.84335
2:        No 14.04012
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. Pelvic Tilt
Distribution:
      0%      25%      50%      75%     100% 
-36.4100  -8.8325  -2.0750   2.1025  14.4200 
Model Type Y: boosting 
RMSE: 11.2204976403802 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 7.62365220568526 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -3.843 (Std.Error: 2.492)
Trimmed ATE (Yes-No): -3.805 (Std.Error: 2.563)
Upper ATE (Yes-No): -4.991 (Std.Error: 3.756)
Observational differences in treatment -3.908 (Yes-No) 

   treatment  outcome
1:       Yes 18.08051
2:        No 21.98834
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. Pelvic Tilt
Distribution:
      0%      25%      50%      75%     100% 
-26.6200  -7.0000  -2.0150   1.7425  23.0000 
Model Type Y: boosting 
RMSE: 9.94756705688136 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 6.82312808674904 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): -8.568 (Std.Error: 1.817)
Trimmed ATE (Yes-No): -8.829 (Std.Error: 1.875)
Upper ATE (Yes-No): -1.976 (Std.Error: 3.988)
Observational differences in treatment -3.237 (Yes-No) 

   treatment  outcome
1:       Yes 19.46765
2:        No 22.70514
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RSA
Distribution:
      0%      25%      50%      75%     100% 
-67.5592 -17.7592  -6.0986   1.8393  15.3720 
Model Type Y: boosting 
RMSE: 14.2396934972892 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

Model Type No: boosting 
RMSE: 11.955728277692 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): -9.238 (Std.Error: 3.357)
Trimmed ATE (Yes-No): -9.29 (Std.Error: 3.473)
Upper ATE (Yes-No): -7.852 (Std.Error: 5.493)
Observational differences in treatment -4.264 (Yes-No) 

   treatment  outcome
1:       Yes  7.98349
2:        No 12.24719
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RSA
Distribution:
      0%      25%      50%      75%     100% 
-62.4716 -16.0000  -5.2168   1.0000  25.0400 
Model Type Y: boosting 
RMSE: 16.1822882172608 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 10.8598848190415 
Params: nrounds: 100.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.625

ATE (Yes-No): -14.888 (Std.Error: 2.875)
Trimmed ATE (Yes-No): -15.078 (Std.Error: 3.055)
Upper ATE (Yes-No): -10.407 (Std.Error: 6.831)
Observational differences in treatment -1.222 (Yes-No) 

   treatment  outcome
1:       Yes 12.21660
2:        No 13.43816
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RPV
Distribution:
       0%       25%       50%       75%      100% 
-13.80090  -2.20050   2.16740   8.20375  35.50390 
Model Type Y: boosting 
RMSE: 10.9302660594733 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 7.1611855014717 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): 4.229 (Std.Error: 2.892)
Trimmed ATE (Yes-No): 4.24 (Std.Error: 2.973)
Upper ATE (Yes-No): 3.885 (Std.Error: 3.908)
Observational differences in treatment 3.489 (Yes-No) 

   treatment   outcome
1:       Yes -4.402080
2:        No -7.891561
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RPV
Distribution:
       0%       25%       50%       75%      100% 
-22.18000  -1.24995   2.33515   6.49505  26.63460 
Model Type Y: boosting 
RMSE: 10.5280489479363 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

Model Type No: boosting 
RMSE: 6.36555163732614 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

ATE (Yes-No): 8.801 (Std.Error: 2.046)
Trimmed ATE (Yes-No): 8.946 (Std.Error: 2.108)
Upper ATE (Yes-No): 4.777 (Std.Error: 3.694)
Observational differences in treatment 1.581 (Yes-No) 

   treatment   outcome
1:       Yes -6.798891
2:        No -8.379701
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 6W. RLL
Distribution:
      0%      25%      50%      75%     100% 
-50.4092  -0.1871   9.2750  24.0000  71.0000 
Model Type Y: boosting 
RMSE: 19.5253607043171 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.6
min_child_weight: 1.0
subsample: 1.0

Model Type No: boosting 
RMSE: 15.698189980176 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): 5.41 (Std.Error: 4.537)
Trimmed ATE (Yes-No): 5.604 (Std.Error: 4.691)
Upper ATE (Yes-No): -0.02 (Std.Error: 5.694)
Observational differences in treatment 2.532 (Yes-No) 

   treatment   outcome
1:       Yes -11.99869
2:        No -14.53073
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Outcome: 1Y. RLL
Distribution:
      0%      25%      50%      75%     100% 
-22.5800  -0.8950   7.4295  24.4364  67.7026 
Model Type Y: boosting 
RMSE: 19.1146388615057 
Params: nrounds: 50.0
max_depth: 1
eta: 0.3
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.75

Model Type No: boosting 
RMSE: 15.0103300181806 
Params: nrounds: 50.0
max_depth: 1
eta: 0.4
gamma: 0.0
colsample_bytree: 0.8
min_child_weight: 1.0
subsample: 0.5

ATE (Yes-No): 11.646 (Std.Error: 4.853)
Trimmed ATE (Yes-No): 11.747 (Std.Error: 5.077)
Upper ATE (Yes-No): 9.105 (Std.Error: 7.061)
Observational differences in treatment 0.506 (Yes-No) 

   treatment   outcome
1:       Yes -14.51151
2:        No -15.01760
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'